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Averaged Circuit Eigenvalue Sampling

Author Steven T. Flammia

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Author Details

Steven T. Flammia
  • AWS Center for Quantum Computing, Pasadena, CA, USA
  • California Institute of Technology, Pasadena, CA, USA

Funding

This work was supported by the ARO QCISS program grant W911NF2110001.

Acknowledgements

We thank Laura DeLorenzo, Robin Harper, Robert Huang, Alex Kubica, Ryan O'Donnell, Colm Ryan, Prasahnt Sivarajah, and Giacomo Torlai for discussions.

Cite As Get BibTex

Steven T. Flammia. Averaged Circuit Eigenvalue Sampling. In 17th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 232, pp. 4:1-4:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.TQC.2022.4

Abstract

We introduce ACES, a method for scalable noise metrology of quantum circuits that stands for Averaged Circuit Eigenvalue Sampling. It simultaneously estimates the individual error rates of all the gates in collections of quantum circuits, and can even account for space and time correlations between these gates. ACES strictly generalizes randomized benchmarking (RB), interleaved RB, simultaneous RB, and several other related techniques. However, ACES provides much more information and provably works under strictly weaker assumptions than these techniques. Finally, ACES is extremely scalable: we demonstrate with numerical simulations that it simultaneously and precisely estimates all the Pauli error rates on every gate and measurement in a 100 qubit quantum device using fewer than 20 relatively shallow Clifford circuits and an experimentally feasible number of samples. By learning the detailed gate errors for large quantum devices, ACES opens new possibilities for error mitigation, bespoke quantum error correcting codes and decoders, customized compilers, and more.

Subject Classification

ACM Subject Classification
  • Hardware → Quantum computation
Keywords
  • Quantum noise estimation
  • quantum benchmarking
  • QCVV

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